Fraudulent Claims Management

After 100 calls with many insurance companies, one trend was clear: they lose millions annually to fraudulent claims. The Coalition Against Insurance Fraud (CAIF) estimates that insurance fraud costs the US $308.6 billion annually. These claims slip through manual review processes due to human error. A mid-sized regional insurer built a new review process using Pulse’s OCR models that goes beyond simple data extraction: - Automatically flags inconsistencies between written statements and - submitted photos - Identifies suspicious patterns like identical damage descriptions across multiple claims - Detects unusual timing patterns that suggest staged accidents - Cross-references medical terminology with reported incidents to validate treatment necessity Business impact in the first few months of deployment: already a 10%+ reduction in fraudulent payouts and 67% faster processing for legitimate claims, leading to millions of annual savings. Excited to see what everyone’s building with Pulse!

🔍 India Uses AI/ML to Detect Healthcare Fraud under AB-PMJAY 🔍 The Government of India is leveraging Artificial Intelligence and Machine Learning to detect fraud, abuse, and overbilling under the Ayushman Bharat – PM-JAY health insurance scheme. 📊 As of August 2023: 24.33 crore Ayushman cards issued ₹6.97 crore in suspicious claims flagged (e.g., for patients after their recorded date of death) 2.15 lakh transactions identified as potentially fraudulent 🛠️ How the Fraud Was Detected AI/ML algorithms analyzed massive claims data using: 📅 Temporal Analysis: Claims filed after recorded death or before diagnosis date 🏥 Clustering & Pattern Recognition: Identifying hospitals submitting identical or statistically unlikely claim patterns 👥 Beneficiary Profiling: Linking multiple claims to the same ID, biometric mismatches, or dead beneficiaries 🔁 Duplicate Treatment Detection: Repeated procedures on the same patient within short time spans across locations 🧾 Natural Language Processing (NLP): Parsing medical records for inconsistencies between diagnosis and treatment 📍 Geo-Tagging & Mobility Analysis: Beneficiaries shown to be in multiple locations at once This is exactly what internal auditor also do to identify suspicious transactions, trsanction time of the day (too early or too late), finding a pattern, top and bottom analysis, frequency analysis, multiple location analysis, etc. 💡 Implications for Internal Auditors & CROs Scalability: AI can process at scale vs human teams — essential for 100% coverage. Proactive Detection: Flag anomalies before reimbursements are processed. Replicable Model: Apply this methodology in procurement claims, reimbursement, loyalty and more. This is not just a government success story — it's a template for every organization fighting document fraud, claims abuse, or control circumvention at scale. Is your audit/risk function leveraging AI for anomaly detection yet? #AI #FraudDetection #RiskManagement #InternalAudit #MachineLearning #DataAnalytics #GovTech #India https://lnkd.in/dMihknaM

What began as a routine fraud claim turned into something far more serious. A customer reported an unrecognized debit charge. The bank followed Regulation E protocol, issuing a provisional credit issued and launching an investigation. Everything checked out. But weeks later, the same person filed another false claim - different bank, same story. Then again, claiming a package never arrived. Then again, after losing an online bet they couldn’t afford. By the time the pattern was spotted, the fraudster had exploited 15 institutions, collecting thousands in cash and goods. This is called dispute abuse, and it's a multi-billion-dollar issue, with bad actors moving across banks, fintechs, eCommerce, and gig platforms. It's why we launched Socure’s Dispute Abuse Score, part of our Sigma First-Party Fraud solution. It’s the first purpose-built score predicting if a real identity will abuse disputes. before a claim is made. Trained on patterns from our First-Party Fraud Consortium, the model spans 20B+ transactions and 350M+ accounts. By spotting repeat abusers early, teams can reduce losses, investigation strain, and protect legitimate users, without compromising experience. If your institution is dealing with first-party fraud, take a look at the blog post below in the comments.